提出TransU模型，让知识图谱更好利用属性间关系。
Embedding Method for Knowledge Graph with Densely Defined Ontology
- 将属性视为实体子集，统一建模知识图谱
- 在标准与实际数据集上提升知识补全效果
- 适合有丰富属性关系的领域知识应用
知识图谱嵌入（KGE）通过弥补知识图谱的不完整性并提升知识检索能力来增强图谱。现有KGE模型的一个局限是未能充分利用本体，特别是属性之间的关系。本文提出一种名为TransU的KGE模型，专为具有明确定义本体的知识图谱设计，能够融合属性间的关系。该模型将属性视为实体的子集，实现统一表示。我们在标准数据集和实际应用场景数据集上进行了实验，验证了模型的有效性。
原文摘要 · Abstract (English)
Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE models is their underutilization of ontologies, specifically the relationships between properties. This study proposes a KGE model, TransU, designed for knowledge graphs with well-defined ontologies that incorporate relationships between properties. The model treats properties as a subset of entities, enabling a unified representation. We present experimental results using a standard dataset and a practical dataset.
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